{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dev\\Anaconda3\\lib\\site-packages\\statsmodels\\compat\\pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
      "  from pandas.core import datetools\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import sklearn as sk\n",
    "import sklearn.linear_model as sk_lm\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>default</th>\n",
       "      <th>student</th>\n",
       "      <th>balance</th>\n",
       "      <th>income</th>\n",
       "      <th>default2</th>\n",
       "      <th>student2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>729.526495</td>\n",
       "      <td>44361.625074</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>817.180407</td>\n",
       "      <td>12106.134700</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>1073.549164</td>\n",
       "      <td>31767.138947</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>529.250605</td>\n",
       "      <td>35704.493935</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>785.655883</td>\n",
       "      <td>38463.495879</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  default student      balance        income  default2  student2\n",
       "1      No      No   729.526495  44361.625074         0         0\n",
       "2      No     Yes   817.180407  12106.134700         0         1\n",
       "3      No      No  1073.549164  31767.138947         0         0\n",
       "4      No      No   529.250605  35704.493935         0         0\n",
       "5      No      No   785.655883  38463.495879         0         0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel('./data/Default.xlsx')\n",
    "\n",
    "df['default2'] = df.default.factorize()[0]\n",
    "df['student2'] = df.student.factorize()[0]\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 10000 entries, 1 to 10000\n",
      "Data columns (total 6 columns):\n",
      "default     10000 non-null object\n",
      "student     10000 non-null object\n",
      "balance     10000 non-null float64\n",
      "income      10000 non-null float64\n",
      "default2    10000 non-null int64\n",
      "student2    10000 non-null int64\n",
      "dtypes: float64(2), int64(2), object(2)\n",
      "memory usage: 546.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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oYChF2QvI2xajaaejwOtHzHhhSKg1oFj0AkYzNuNZh0sll+G0xfHhLKOZ1AoB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\nP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aa3CSCrDC8stlif5FwbZby+NcGXUzzpBvwXltpc2BnzzEKLlu/IqLaOKKrmtbK4smPT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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b5196ed898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12, 5))\n",
    "gs = mpl.gridspec.GridSpec(1, 4)\n",
    "\n",
    "ax1 = plt.subplot(gs[0, : -2])\n",
    "ax2 = plt.subplot(gs[0, -2])\n",
    "ax3 = plt.subplot(gs[0, -1])\n",
    "\n",
    "df_no = df[df.default2 == 0].sample(frac=0.15)\n",
    "df_yes = df[df.default2 == 1]\n",
    "df_ = df_no.append(df_yes)\n",
    "\n",
    "ax1.scatter(df_[df_.default == 'Yes'].balance, df_[df_.default == 'Yes'].income, s=40, c='orange', marker='+',\n",
    "            linewidths=1)\n",
    "ax1.scatter(df_[df_.default == 'No'].balance, df_[df_.default == 'No'].income, s=40, marker='o', linewidths='1',\n",
    "            edgecolors='lightblue', facecolors='white', alpha=.6)\n",
    "\n",
    "ax1.set_ylim(ymin=0)\n",
    "ax1.set_ylabel('Income')\n",
    "ax1.set_xlim(xmin=-100)\n",
    "ax1.set_xlabel('Balance')\n",
    "\n",
    "c_palette = {'No':'lightblue', 'Yes':'orange'}\n",
    "sns.boxplot('default', 'balance', data=df, orient='v', ax=ax2, palette=c_palette)\n",
    "sns.boxplot('default', 'income', data=df, orient='v', ax=ax3, palette=c_palette)\n",
    "gs.tight_layout(plt.gcf())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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p0BYRERGRJfPwsSHi6SxbT1tGtKG2Q9Ha7p2IiIiIVIyjI0n2D8TZ1N3Gitamcjen5BRo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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b51af9b630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_train = df.balance.values.reshape(-1, 1)\n",
    "y = df.default2\n",
    "\n",
    "X_test = np.arange(df.balance.min(), df.balance.max()).reshape(-1, 1)\n",
    "\n",
    "clf = sk_lm.LogisticRegression()\n",
    "clf.fit(X_train, y)\n",
    "prob = clf.predict_proba(X_test)\n",
    "\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(1,2, figsize=(12,5))\n",
    "\n",
    "# Left plot\n",
    "sns.regplot(df.balance, df.default2, order=1, ci=None,\n",
    "            scatter_kws={'color':'orange'},\n",
    "            line_kws={'color':'lightblue', 'lw':2}, ax=ax1)\n",
    "# Right plot\n",
    "ax2.scatter(X_train, y, color='orange')\n",
    "ax2.plot(X_test, prob[:,1], color='lightblue')\n",
    "\n",
    "for ax in fig.axes:\n",
    "    ax.hlines(1, xmin=ax.xaxis.get_data_interval()[0],\n",
    "              xmax=ax.xaxis.get_data_interval()[1], linestyles='dashed', lw=1)\n",
    "    ax.hlines(0, xmin=ax.xaxis.get_data_interval()[0],\n",
    "              xmax=ax.xaxis.get_data_interval()[1], linestyles='dashed', lw=1)\n",
    "    ax.set_ylabel('Probability of default')\n",
    "    ax.set_xlabel('Balance')\n",
    "    ax.set_yticks([0, 0.25, 0.5, 0.75, 1.])\n",
    "    ax.set_xlim(xmin=-100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y = df.default2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
      "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
      "          penalty='l2', random_state=None, solver='newton-cg', tol=0.0001,\n",
      "          verbose=0, warm_start=False)\n",
      "classes:  [0 1]\n",
      "coefficients:  [[ 0.00549892]]\n",
      "intercept : [-10.65133001]\n"
     ]
    }
   ],
   "source": [
    "clf = sk_lm.LogisticRegression(solver='newton-cg')\n",
    "X_train = df.balance.values.reshape(-1,1)\n",
    "clf.fit(X_train,y)\n",
    "print(clf)\n",
    "print('classes: ',clf.classes_)\n",
    "print('coefficients: ',clf.coef_)\n",
    "print('intercept :', clf.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.079823\n",
      "         Iterations 10\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Coef.</th>\n",
       "      <th>Std.Err.</th>\n",
       "      <th>z</th>\n",
       "      <th>P&gt;|z|</th>\n",
       "      <th>[0.025</th>\n",
       "      <th>0.975]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>const</th>\n",
       "      <td>-10.651331</td>\n",
       "      <td>0.361169</td>\n",
       "      <td>-29.491287</td>\n",
       "      <td>3.723665e-191</td>\n",
       "      <td>-11.359208</td>\n",
       "      <td>-9.943453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>balance</th>\n",
       "      <td>0.005499</td>\n",
       "      <td>0.000220</td>\n",
       "      <td>24.952404</td>\n",
       "      <td>2.010855e-137</td>\n",
       "      <td>0.005067</td>\n",
       "      <td>0.005931</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Coef.  Std.Err.          z          P>|z|     [0.025    0.975]\n",
       "const   -10.651331  0.361169 -29.491287  3.723665e-191 -11.359208 -9.943453\n",
       "balance   0.005499  0.000220  24.952404  2.010855e-137   0.005067  0.005931"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = sm.add_constant(df.balance)\n",
    "est = smf.Logit(y.ravel(), X_train).fit()\n",
    "est.summary2().tables[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.145434\n",
      "         Iterations 7\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Coef.</th>\n",
       "      <th>Std.Err.</th>\n",
       "      <th>z</th>\n",
       "      <th>P&gt;|z|</th>\n",
       "      <th>[0.025</th>\n",
       "      <th>0.975]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>const</th>\n",
       "      <td>-3.504128</td>\n",
       "      <td>0.070713</td>\n",
       "      <td>-49.554094</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-3.642723</td>\n",
       "      <td>-3.365532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>student2</th>\n",
       "      <td>0.404887</td>\n",
       "      <td>0.115019</td>\n",
       "      <td>3.520177</td>\n",
       "      <td>0.000431</td>\n",
       "      <td>0.179454</td>\n",
       "      <td>0.630320</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Coef.  Std.Err.          z     P>|z|    [0.025    0.975]\n",
       "const    -3.504128  0.070713 -49.554094  0.000000 -3.642723 -3.365532\n",
       "student2  0.404887  0.115019   3.520177  0.000431  0.179454  0.630320"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = sm.add_constant(df.student2)\n",
    "y = df.default2\n",
    "\n",
    "est = smf.Logit(y.ravel(), X_train).fit()\n",
    "est.summary2().tables[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.078577\n",
      "         Iterations 10\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Coef.</th>\n",
       "      <th>Std.Err.</th>\n",
       "      <th>z</th>\n",
       "      <th>P&gt;|z|</th>\n",
       "      <th>[0.025</th>\n",
       "      <th>0.975]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>const</th>\n",
       "      <td>-10.869045</td>\n",
       "      <td>0.492273</td>\n",
       "      <td>-22.079320</td>\n",
       "      <td>4.995499e-108</td>\n",
       "      <td>-11.833882</td>\n",
       "      <td>-9.904209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>balance</th>\n",
       "      <td>0.005737</td>\n",
       "      <td>0.000232</td>\n",
       "      <td>24.736506</td>\n",
       "      <td>4.331521e-135</td>\n",
       "      <td>0.005282</td>\n",
       "      <td>0.006191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>income</th>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.369808</td>\n",
       "      <td>7.115254e-01</td>\n",
       "      <td>-0.000013</td>\n",
       "      <td>0.000019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>student2</th>\n",
       "      <td>-0.646776</td>\n",
       "      <td>0.236257</td>\n",
       "      <td>-2.737595</td>\n",
       "      <td>6.189022e-03</td>\n",
       "      <td>-1.109831</td>\n",
       "      <td>-0.183721</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Coef.  Std.Err.          z          P>|z|     [0.025    0.975]\n",
       "const    -10.869045  0.492273 -22.079320  4.995499e-108 -11.833882 -9.904209\n",
       "balance    0.005737  0.000232  24.736506  4.331521e-135   0.005282  0.006191\n",
       "income     0.000003  0.000008   0.369808   7.115254e-01  -0.000013  0.000019\n",
       "student2  -0.646776  0.236257  -2.737595   6.189022e-03  -1.109831 -0.183721"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "X_train = sm.add_constant(df[['balance', 'income', 'student2']])\n",
    "est = smf.Logit(y, X_train).fit()\n",
    "est.summary2().tables[1]"
   ]
  }
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